1. Field of the Invention
The present invention relates to power generation facilities, and particularly to a particle swarm optimization system and method for microgrids.
2. Description of the Related Art
Power system construction has changed in the last two decades due to a lot of challenges, such as load growth, new environmental policy with a demand to reduce CO2 emissions, and marketplace economic stresses. This has led to increased interest in local connection of renewable energy sources at the distribution level. Microgrids are small electrical distribution systems that connect multiple customers to multiple distributed sources of generation and storage through power electronic inverters, which provide the necessary interface. Microgrids can be operated in both autonomous and grid-connected modes. There exists a requirement to control the microgrid in both autonomous and grid-connected mode. Thus, microgrid control in both modes has been investigated recently. Control issues include keeping bus voltages within specified limits; controlling transformer, line and feeder loading; minimizing active power losses; managing reactive power sources; and controlling the power factor.
A microgrid power system is a local scale power system that uses microsource generation scaled either by electrical or thermal output to the local system demand. It can serve a customer with multiple load locations, an industrial park, or a campus. It is designed to transfer seamlessly between connection with the local utility and isolated operation. It provides power reliability and power quality benefits not available from the conventional utility grid system. Moreover, it incorporates communication/aggregation features to allow organization and control of the microgrid power system as a single entity.
The dynamic nature of the distribution network of a microgrid challenges the stability and control effectiveness of the microgrids in both grid-connected and autonomous modes. In the grid-connected mode, control of the inverter is required to make the microgrid capable of regulating the active and reactive output currents, ensuring high power quality levels, and achieving relative immunity to grid perturbations. In the autonomous mode, the inverter is controlled to feed the load with the pre-defined voltage and frequency values according to a specific control strategy.
Generally, two control loops are used in the autonomous mode. The inner loop includes voltage and current PI controllers, which are designed to reject high frequency disturbances and damp the output filter to avoid resonance with the external network. The outer power loop is based on well-known droop control to share the fundamental real and reactive powers with other microgrid sources. In droop control, the inverter emulates the behavior of a synchronous machine. The power angled δ depends mainly on real power P, while the voltage depends mainly on reactive power Q. In other words, the angle δ can be controlled by regulating P, while the output inverter voltage can be controlled through Q. Control of frequency dynamically controls the power angle, and thus the real power flow.
Therefore, by adjusting P and Q independently, frequency and voltage amplitude of the microgrid can be determined. The stability of the microgrids operating in either mode is quite essential, and is affected by different parameters. In the autonomous mode, stability can be affected by controller parameters, as well as by power sharing coefficients. In the grid-connected mode, controller parameters and filter parameters are the key factors of microgrid stability.
Generally, careful selection of the controller, filter, and power sharing parameters maintains power quality within the regulated range and enhances system performance against load changes and disturbances. Different approaches to select the controller parameters and control strategies have been reported in the literature, where a trial and error approach has been adopted. This approach is time-consuming, and there is no guarantee that the adopted settings are the optimal ones. In addition, it does not provide a systematic procedure to solve the controller design problem.
Recently, computational intelligence algorithms, such as the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm have been applied to different power system problems with impressive success. However, some deficiencies in GA performance, such as premature convergence, have been recorded. On the other hand, PSO has been widely implemented and stamped as one of the promising optimization techniques due its simplicity, computational efficiency, and robustness. Generally, PSO has been motivated by the behavior of organisms, such as fish schools and bird flocks. It combines social psychology principles in socio-cognition human agents and evolutionary computations. Unlike the other heuristic techniques, PSO has a flexible and well-balanced mechanism to enhance global and local exploration abilities.
Although microgrid planning and operation have several challenging problems, such as size, location, and optimal design of different controllers, the application of PSO to solve these problems is still in its early stage. Optimal size and location problem of the distributed generation unit (DG) has been addressed for maximizing the economic benefits and minimizing the line loss. PSO has also been also employed to obtain controller parameters and power sharing coefficients in both modes. However, the LC filter design and coupling inductance have not been taken into consideration. It is worth mentioning that microgrid stability is strongly affected also by the filter parameters. While a PSO algorithm has been applied directly to a power-electronic-switch-level microgrid simulation model instead of small-signal models, the aforementioned method needs external software to simulate the system and calculate the objective function.
Thus, a particle swarm optimization system and method for microgrids solving the aforementioned problems is desired.